Application of machine/deep-learning to the systems biology of glycosylation

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The NHLBI grant “Systems Biology of Glycosylation” aims to apply biomolecular engineering approaches to study blood cell glycosylation from both a basic science and translational perspective. The goal is to develop a quantitative link between the cellular transcriptome and epigenetic status, with the resulting glycosylation profile. A portion of the grant is focused on discovering the cellular regulatory pathways in blood cells that control the pattern of glycosylation on these cells, and assessing the extent to which these principles are generalizable. In a second aspect, using this new knowledge, we determine if measuring selected genetic signatures can report on the glycosylation status of cells. The identification of these key makers/checkpoints has translational significance as it can inform both patient stratification in the context of clinical trials and precision medicine applications. In order to achieve these objectives, two types of perturbation experiments are performed using different blood cells. In the first, CRISPR-Cas9/gRNA is used to implement defined system perturbations and resulting changes in the cellular glycome are measured. This represents the ‘labeled dataset’ from the Machine Learning/Deep Learning (ML/DL) perspective. In the second, biochemical stimuli are applied to perturb cell state, and again cell glycosylation status measurements are made. This is the ’unlabeled dataset’ as the perturbation is imprecise. In each case several experimental outputs or ‘features’ are measured including: 1) Single-cell next- generation sequencing (NGS) for the simultaneous quantitation of the underlying transcriptome, nature of gRNA (guide-RNA) perturbation and glycosylation status (using lectin binding), on individual cells. 2) Spectral flow cytometry to measure fluorescent lectin binding in larger scale, with the option that selected rare cell types could be sorted for more in-depth profiling. 3) Mass spectrometry to obtain detailed glycan structure data. Mathematical methods are developed to fuse results from these different omics-methods and develop input-output responses. Currently, such modeling relies on prior biochemical knowledge that is curated in pathway maps, linear-mixed models and explicit programming. As an alternative to this traditional approach, this supplement will prepare the data for ML/DL modeling and related learning. To achieve this, we add two new expert investigators to this project: Gunawan (systems biology, single-cell analysis) and Chen (machine/deep learning). The specific aims will: 1) Collect sufficient data for ML/DL; 2) Normalize and standardize these data for ML/DL readiness; and 3) Use the transformed data in pilot ML/DL tests. Successful project completion will confirm the value of ML/DL in the study of blood cell and Glycoscience applications. To our best knowledge, this would represent the first application of ML/DL to multi-omics Glycosciences. A comparison with traditional modeling methods that are already supported in the funded application, will tell us about the merits and limitations of ML/DL. Finally, a general ML/DL data processing framework will emerge that can be applied to other aspects of this project and also other related biomedical problems.